Live time-lapse dataset ofﾠin vitroﾠwound healing experiments

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dc.citation.firstpage1en_US
dc.citation.issueNumber1en_US
dc.citation.journalTitleGigascienceen_US
dc.citation.lastpage5en_US
dc.citation.volumeNumber4en_US
dc.contributor.authorZaritsky, Assafen_US
dc.contributor.authorNatan, Sarien_US
dc.contributor.authorKaplan, Doronen_US
dc.contributor.authorBen-Jacob, Eshelen_US
dc.contributor.authorTsarfaty, Ilanen_US
dc.date.accessioned2017-05-22T18:57:18Z
dc.date.available2017-05-22T18:57:18Z
dc.date.issued2015en_US
dc.description.abstractBackground: The wound healing assay is the common method to study collective cell migration in vitro. Computational analyses of live imaging exploit the rich temporal information and significantly improve understanding of complex phenomena that emerge during this mode of collective motility. Publicly available experimental data can allow application of new analyses to promote new discoveries, and assess algorithms’ capabilities to distinguish between different experimental conditions. Findings: A freely-available dataset of 31 time-lapse in vitro wound healing experiments of two cell lines is presented. It consists of six different experimental conditions with 4–6 replicates each, gathered to study the effects of a growth factor on collective cell migration. The raw data is available at ‘The Cell: an Image Library’ repository. This Data Note provides detailed description of the data, intermediately processed data, scripts and experimental validations that have not been reported before and are currently available at GigaDB. This is the first publicly available repository of live collective cell migration data that includes independent replicates for each set of conditions. Conclusions: This dataset has the potential for extensive reuse. Some aspects in the data remain unexplored and can be exploited extensively to reveal new insight. The dataset could also be used to assess the performance of available and new quantification methods by demonstrating phenotypic discriminatory capabilities between the different experimental conditions. It may allow faster and more elaborated, reproducible and effective analyses, which will likely lead to new biological and biophysical discoveries.en_US
dc.identifier.citationZaritsky, Assaf, Natan, Sari, Kaplan, Doron, et al.. "Live time-lapse dataset ofﾠin vitroﾠwound healing experiments." <i>Gigascience,</i> 4, no. 1 (2015) Oxford University Press: 1-5. https://doi.org/10.1186/s13742-015-0049-6.
dc.identifier.doihttps://doi.org/10.1186/s13742-015-0049-6en_US
dc.identifier.urihttps://hdl.handle.net/1911/94329
dc.language.isoengen_US
dc.publisherOxford University Press
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subject.keywordcollective cell migrationen_US
dc.subject.keywordwound healing assayen_US
dc.subject.keywordHGF/SF-Meten_US
dc.subject.keywordimage analysisen_US
dc.subject.keywordlive cell imagingen_US
dc.titleLive time-lapse dataset ofﾠin vitroﾠwound healing experimentsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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